Variable Clustering with Multiple Latent Components Clustering is based on k-means algorithm. In each step cluster centers are few PCA components, computed for variables in that cluster. The distance is defined by R^2 (obtained by performing least-squares).
The main function of package varclust is
mlcc.bic which allows clustering variables in a data
with unknown number of clusters. Variable partition is computed
with k-means based algorithm. Number of clusters and their dimensions
are computed using BIC criterion.
If the number of clusters is known one might use function
which takes number of clusters as a parameter. For
mlcc.reps one might
specify as well some initial segmentation for k-means algorithm. This can be useful if
user has some apriori knowledge about clustering.
We also provide function
misclassification that computes misclassification
rate between two partitions. This performance measure is
extensively used in image segmentation.
Piotr Sobczyk, Julie Josse
Maintainer: Piotr Sobczyk [email protected]
Piotr Sobczyk, Malgorzata Bogdan, Julie Josse, Clustering around latent variables - a technical report, 2014, www.im.pwr.edu.pl/~sobczyk/research.html
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